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510(k) Data Aggregation

    K Number
    K162306
    Device Name
    SomnoDent ALPHA
    Manufacturer
    Date Cleared
    2016-09-20

    (34 days)

    Product Code
    Regulation Number
    872.5570
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    SomnoDent ALPHA

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The SomnoDent ALPHA is temporarily intended as an aid in the reduction of simple night time snoring and mild to moderate obstructive sleep apnea (OSA) in patients 18 years and older.

    Device Description

    The SomnoDent ALPHA is a device that functions as a mandibular repositioner, which acts to increase the patient's pharyngeal space during sleep. The increase in the patient's pharyngeal space improves their ability to exchange air during sleep. The device consists of two generic splints, which fit over the upper and lower teeth. The lower splint is held in a protrusive position by an advancement mechanism. The device advances the mandible in the sagittal plane to increase the patient's pharyngeal space during sleep and reduce the apnea symptoms.

    AI/ML Overview

    This document is a 510(k) summary for the SomnoDent ALPHA® device, which is an intraoral device for snoring and obstructive sleep apnea. The document asserts the device's substantial equivalence to predicate devices based on technological characteristics, intended use, and performance testing.

    There is no detailed study described in this document that proves the device meets specific acceptance criteria with quantifiable results. Instead, the document relies on comparisons to predicate devices and general statements about performance and safety testing.

    Here's a breakdown of the requested information based on the provided text, highlighting the absence of specific study details where applicable:


    1. Table of Acceptance Criteria and Reported Device Performance

    The document does not explicitly present a table of acceptance criteria with specific numerical targets. It relies on the concept of "substantial equivalence" to predicate devices. However, we can infer some "performance specifications" from the comparison table (Section 7.0) and the discussion in Section 8.0.

    Acceptance Criteria (Inferred/Stated)Reported Device Performance (SomnoDent ALPHA®)
    Intended Use: Aid in reduction of simple night time snoring and mild to moderate obstructive sleep apnea (OSA) in patients 18 years and older.Similar intended use as primary and reference predicates, with the added temporary use claim ("temporarily intended as an aid...").
    Operating Principle: Mandibular AdvancementFunctions as a mandibular repositioner, increasing pharyngeal space.
    Adjustment Range: Necessary for establishing starting protrusion and therapeutic adjustment.0-20 mm (compared to 0-8 mm for primary predicate and "Same" for reference predicate, implying it meets or exceeds the necessary range for customizable devices).
    Durability of Adjustment Mechanism: Withstand applicable forces within the mouth.Testing on the adjustment mechanism (wing/lug, clip lock) demonstrates it can withstand applicable forces.
    Biocompatibility: Non-sensitizer, non-irritant, non-cytotoxic.Biocompatibility evaluation and testing to ISO 10993 demonstrated the device is a non-sensitizer, non-irritant, and non-cytotoxic.
    Safety and Effectiveness: Perform as intended and be safe and effective.Risk management (ISO 14971) and performance testing conducted. Concluded that the SomnoDent ALPHA is "state of the art with acceptable risks." Simulated in-use testing (mechanical cyclic force testing) supported the in-use claim.
    In-use Claim: Temporarily intended as an aid. (Specific duration not explicitly an acceptance criterion but a device characteristic)90 days. Supported by biocompatibility and in-use bench testing.

    2. Sample Size Used for the Test Set and the Data Provenance

    This information is not provided in the document. The document refers to "performance testing" and "simulated in-use testing" but does not specify the sample size for these tests (e.g., number of devices tested, number of cycles for mechanical testing). Data provenance is also not mentioned.


    3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

    This information is not provided in the document. The document does not describe any human-involved assessment of the device's performance that would require establishing a ground truth by experts.


    4. Adjudication Method for the Test Set

    This information is not provided in the document. No adjudication method is mentioned as there's no description of a study involving multiple interpretations needing reconciliation.


    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

    There is no MRMC comparative effectiveness study described. This device is a physical intraoral device, not an AI-powered diagnostic or assistive tool, so this type of study is not relevant.


    6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done

    This is not applicable as the device is a physical intraoral device, not an algorithm.


    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

    The concept of "ground truth" as typically applied to diagnostic algorithms is not directly applicable here. The document describes performance testing based on engineering principles (e.g., ability to withstand forces, biocompatibility testing against ISO standards) rather than clinical diagnostic accuracy against a definitive truth.


    8. The sample size for the training set

    This information is not provided and is not applicable as this is a physical device, not a machine learning model requiring a training set.


    9. How the ground truth for the training set was established

    This information is not provided and is not applicable as this is a physical device, not a machine learning model.

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